ABSTRACT

Clustering is the art of subset from a dataset. It helps in identifying the hidden information and arranging data into its logical group based on an attribute or a set of attributes. The intent of this paper is to explore a variety of clustering methods and brief their working styles so that researches can have a partial view of methods discussed. This work presents gains and pitfalls and also associated worst case complexities of each clustering method to some extent. The techniques discussed here are just a snap shot of clustering algorithms. Currently model based algorithms are been used to improve efficiency of clustering algorithms. This paper would be helpful in devising the choice of algorithm for such a purpose.